An obstruction for q-deformation of the convolution product

We consider two independent q-Gaussian random variables X and Y and a function f chosen in such a way that f(X) and X have the same distribution. For 0 < q < 1 we find that at least the fourth moments of X + Y and f(X) + Y are different. We conclude that no q-deformed convolution product can exist for functions of independent q-Gaussian random variables.Comment: The proof of proposition 2 is corrected on 11 january 199

Book Review on The Philosophical Challenge from China (Edited by Brian Bruya)

In this paper, I review the book The Philosophical Challenge from China, edited by Brian Bruya. I critically discuss each of the 13 contributions

Open-ocean interior moored sensor turbulence estimates, below a Meddy

A one-year time series of moored high-resolution temperature T-sensor data from 1455 m depth on a 3900 m long line in about 5300 m of water in the NE-Atlantic Canary Basin are dominated by salinity (over-)compensated intrusions arising from the effects of Mediterranean outflow waters, which are commonly organized as Meddies. During the passage of a Meddy-core above the T-sensors, no intrusions were observed, thereby making it possible to use the temperature records to quantify turbulence parameters. The present data show that these ocean-interior turbulence estimates are from short-lived (less than 0.5 h) rather intense overturning cells with vertical scales of <5 m. Because the turbulence inertial subrange is found to extend into the internal wave band, the overturns are predominantly driven by shear associated with inertial currents. Kinetic energy, current shear and temperature variance peak at sub-inertial frequencies during the Meddy passage, suggesting wave trapping in the warm anti-cyclonic eddy and/or weakly stratified layers. The observations further show that internal wave displacements are coherent over vertical scales of up to 40 m during the presence of the Meddy compared with vertical coherence scales of less than 25 m during the more common no-Meddy conditions of double diffusion intrusions.Comment: 32 pages, 9 figure

Most Peers Don’t Believe It, Hence It Is Probably False

Rob Lovering has recently argued that since theists have been unable, by means of philosophical arguments, to convince 85 percent of professional philosophers that God exists, at least one of their defining beliefs must be either false or meaningless. This paper is a critical examination of his argument. First we present Lovering’s argument and point out its salient features. Next we explain why the argument’s conclusion is entirely acceptable for theists, even if, as we show, there are multiple problems with the premises

Migration and child labour: Exploring child migrant vulnerabilities and those of children left-behind

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILO_MigrationandChildLabour.pdf: 1627 downloads, before Oct. 1, 2020

Multilevel Sparse Grid Methods for Elliptic Partial Differential Equations with Random Coefficients

Stochastic sampling methods are arguably the most direct and least intrusive means of incorporating parametric uncertainty into numerical simulations of partial differential equations with random inputs. However, to achieve an overall error that is within a desired tolerance, a large number of sample simulations may be required (to control the sampling error), each of which may need to be run at high levels of spatial fidelity (to control the spatial error). Multilevel sampling methods aim to achieve the same accuracy as traditional sampling methods, but at a reduced computational cost, through the use of a hierarchy of spatial discretization models. Multilevel algorithms coordinate the number of samples needed at each discretization level by minimizing the computational cost, subject to a given error tolerance. They can be applied to a variety of sampling schemes, exploit nesting when available, can be implemented in parallel and can be used to inform adaptive spatial refinement strategies. We extend the multilevel sampling algorithm to sparse grid stochastic collocation methods, discuss its numerical implementation and demonstrate its efficiency both theoretically and by means of numerical examples